As cloud computing develops rapidly, the energy consumption of large-scale datacenters becomes unneglectable, and thus renewable energy is considered as the extra supply for building sustainable cloud infrastructures. In this chapter, we present a green-aware virtual machine (VM) migration strategy in such datacenters powered by sustainable energy sources, considering the power consumption of both IT functional devices and cooling devices. We define an overall optimization problem from an energy-aware point of view and try to solve it using statistical searching approaches. The purpose is to utilize green energy sufficiently while guaranteeing the performance of applications hosted by the datacenter. Evaluation experiments are conducted under realistic workload traces and solar energy generation data in order to validate the feasibility. Results show that the green energy utilization increases remarkably, and more overall revenues could be achieved.
Abstract-Since huge power consumption of large data centers has become a crucial problem recently, power models, especially precise models, turn out to be important for service providers to learn about the application status in order to make wise decisions. In this paper, we focus on the power modeling of typical applications upon Linux platforms, including CPU-intensive applications, memory-intensive applications, and networkintensive applications. We established models for these different types of applications respectively based on the collection of massive realistic data and further calibrated these models. Error analysis was also given after comparing the computed values with the actual measured data. Finally, synthetic model for the hybrid application execution scenario was figured out and discussed.
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